@synthoelectro
now that's some quantum technology, man... Being one of the beta testers of Stable Diffusion helps me understand this even more.
@CharlieYou823
you're so beautiful and explain the Diffusion model in the most simple way. as the chinese saying: 人狠话不多！:rocket-red-countdown-liftoff:
@paramino
This is very good intro for quick understanding of the concept 👍
@MrMc2BOB
Your explanation helped me a lot to better understand this interesting process. The only technical term I had to look up was: neural convolutional network. This technical term refers to a digital brain that is specially trained to recognize visual patterns. It is characterized by its ability to identify local features in images and process this information hierarchically. All in all, thank You for your explanation
@GouravKhanduri
Learnt a lot about diffusion models, thanks for the video
@jenzi8944
Very clear intro, thanks!
@Simplegrandeur1162
Great video for beginners! Really helpful, Thank you!
@cosmingurau
Sorry, but I don't understand something very important. WHY would you add the noise and then substract the noise? Correct me if I'm wrong, but the rightmost noise image in this example is basically an encoded image of the original dog image, that can be decoded deterministically with the neural network, in multiple steps. That's nice and dandy. And I do understand that the noise image is not like a RAR archive, which, were it to be slightly modified, would just yield corruption errors, and instead the modified noise image would still generate... an image. NOW. 1. How do you get from the user text prompt to the noise image of what the user WANTED, that will THEN be denoised (decoded)? 2. How is it so that not every OTHER noise result from the text prompt (except previously deterministically encoded images like this dog image for example) will output just a bunch of garbled mess? And yes, I know that is sometimes the case, I used Stable Diffusion daily.
@yousufmamsa
Great explanation of diffusion models. Thank you.
@mr.osophy
Such a great video to dive in! I'm live streaming learning about Diffusion, right now!
@sinsernadeesoyo
This video was awesome! Well done :) and thank you
@Kaleubs
Thanks for this video, this was very insightfull. Still have a lot to learn about this topic that will revolutionize our world so much
@malikfahadsarwar2281
It would be good if you also explain the reverse process in detail as you explained the forward process
@zhaoyufei9096
really good video! I have checked few blogs explain how diffusion mode works, still can not understand. But after see your video one time , i have a better understanding how diffusion actually works! Really thanks!
@user-wr4yl7tx3w
This was so helpful. Love this format of starting easier and add layers of explanations.
@AIMLDLNLP-TECH
Appreciate your explanation skill. Q. What is diffusion model Ans. Let's say you tell your best friend, Sarah, about this amazing new flavor. Sarah gets excited and tells her friend, Tom. Then Tom tells his cousin, Emily. Emily, in turn, tells her family, and the news keeps spreading from person to person, creating a chain reaction. This process of your ice cream flavor information spreading from one person to another is like how a drop of ink spreads in water. At first, it's just a small spot, but then it spreads out and covers more and more area as time goes on. In the diffusion model, experts study how things, whether it's information, ideas, or products like your ice cream flavor, spread through a community of people. They try to understand how fast it spreads, how many people it reaches, and what factors influence its spread. By understanding these patterns, they can learn a lot about how people share and adopt new things!
@MrAlextorex
Diffusion models actually predict a bit of noise to remove from the input noisy image at inference time. The noise is added to images just to produce training data.
@John-eq8cu
I want to understand diffusion models so I can understand how it's possible for artificial intelligence to produce an image. Your explanation helps. A bit.
@inetmiguel
Nice explanation! I feel like the video title is misleading, it is just one explanation going deeper and not complete without the deeper levels of knowledge and differs a lot from other videos that start from zero the explanation at different levels. This is more like 4 shades of Diffussion :D Thanks for sharing!
@pi5549